While the concept of social software and social networks is gaining recognition, users may not be able to review and optimize, their personal or professional social network. Users are increasingly suffering from information and conversation overload and would like to identify the most important and the most relevant information and conversations to optimize the allocation of their attention. Accurately calculating a person's social network based on real time communication actions enables significant improvement in personal and professional productivity.
The status of a person in a social context or network is commonly defined in terms of two factors: the total number of endorsements the person receives from other people and the prestige of those endorsers. These two factors indicate the distinction between popularity and expert appreciation or prestige of someone respectively. This framework of popularity and prestige has been commonly applied in the assessment and ranking of scholarly writings based on citations and status of references. It has also been the basis for today's some of today's Internet search engines.
The founders of Google's search engine defined an algorithm—the PageRank algorithm—to assess the rank of web pages by determining the popularity and prestige of referring (e.g. linked) web pages. Modeled on the framework for ranking scholarly writings, the PageRank algorithm has become the standard to evaluate the status of web pages and the engine to determine the relevance of web content and advertising based on a Google user's search queries. PageRank indexes web pages, filters them and presents the most relevant information to the user.